Soil health assessment methods: Progress, applications and comparison

Abstract

Soil health assessment tools are needed to quantify effectiveness of various agricultural practices toward meeting sustainable development goals. Although several soil health tools have been developed and tested through global soil management research, ease of use and site-specific accuracy for farmers and agronomists needs to be optimized. This comprehensive review examines the theories, compares approaches, and examines applications of five soil health assessment methods, and then compares their advantages, disadvantages, application limitations, and feasibility before suggesting potential improvements at various scales. The two predominant soil health assessment tools [Soil Management Assessment Framework (SMAF) and Cornell's Comprehensive Assessment of Soil Health (CASH)] were coupled with six classical mathematical models [Principal Component Analysis, Analytic Hierarchy Process, Iterative Algorithm, Entropy weight method, Euclidean distance and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)] to create 11 approaches that were compared using field-based soil health indicator measurements. The data were collected from field experiments with cover crops and soil amendments in Mississippi, USA. The Standard Scoring Functions (SSF) associated with the SMAF and the CASH tools were evaluated. Our results, reflecting different data normalization and weighting, created 14 different soil health scores that showed significant differences based on method. Among the three data normalization methods (CASH, SSF, and entropy weighting), soil health scores using SSF were relatively high, while those using entropy weighting were much lower. The latter method, TOPSIS, had the advantage of being able to maximize differences among treatments and thus can help select an optimal management solution. Scores obtained through SSF, SSF + PCA and SSF + AHP had some of the best correlations) with corn ( Zea mays L. ) and soybean [ Glycine max ( Merr. ) L. ] yields, indicating the SSF parameters selected for our study were applicable. CASH provided similar results with a more simplistic approach. Other methods generated soil health scores with poorer fits when compared to the yield data. Overall, we conclude all 11 methods and 14 soil health scores can be useful for soil health evaluation in the study area. The results re-emphasized that soil health assessment is useful for soil researchers, farmers, and any other stakeholder group wanting to determine if specific agricultural practices contribute to sustainable development.